A tool for analyzing the evolution of non-uniformities in lithium-ion cylindrical battery cells at the module level under various operating conditions
| dc.contributor.author | Dange, Soham Suneel | en |
| dc.contributor.committeechair | Huxtable, Scott T. | en |
| dc.contributor.committeemember | Paul, Mark R. | en |
| dc.contributor.committeemember | Ellis, Michael W. | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2025-01-23T09:00:31Z | |
| dc.date.available | 2025-01-23T09:00:31Z | |
| dc.date.issued | 2025-01-22 | |
| dc.description.abstract | Lithium-ion batteries are critical components in electric vehicles, portable electronics, and grid energy storage systems, necessitating advanced modeling techniques to enhance their safety, performance, and lifespan. This thesis presents the development and validation of a coupled electrical and lumped thermal model for cylindrical lithium-ion batteries along with a finite difference thermal model for spatial temperature prediction of cylindrical cell These models address key challenges in simulating real-world battery behavior. The electrical model utilizes a 2 R-C pair equivalent circuit framework integrated with a busbar model to account for current imbalances in parallel-connected cells. This model is a common equivalent circuit model used to represent a Li-ion cell using a voltage source, series resistor, and two resistor-capacitor pair connected in parallel. A lumped thermal model coupled with the electrical framework dynamically adjusts parameters based on temperature variations, achieving a voltage prediction error of less than 200 mV. Additionally, the thermal model employs a finite difference method (FDM) to solve the 3D transient heat conduction equation, providing spatial temperature distribution within cells and capturing critical gradients between core and surface temperatures. The vectorization of the thermal solver reduced simulation time by half, and its validation against Ansys™ simulations and module-level data demonstrated temperature prediction accuracy within a 2–3°C margin. The developed tool is scalable for any number of cylindrical cells arranged in a rectangular grid, addressing key gaps identified in the literature, including the need to simulate spatial and temporal non-uniformities in state-of-charge (SOC), state-of-health (SOH), and temperature, which significantly affect battery performance and lifespan. It provides a scalable, efficient tool for predicting thermal and electrical behavior across cell and module levels. This work contributes to the development of a tool that will, enable informed design decisions for next-generation energy storage systems. Future research could focus on extending the model to incorporate aging effects, enhanced thermal management configurations, and real-time simulations for battery management systems. | en |
| dc.description.abstractgeneral | Lithium-ion batteries play a crucial role in powering electric vehicles, smartphones, and renewable energy storage systems. As demand for these technologies grows, ensuring that batteries operate safely and efficiently becomes increasingly important. This research focuses on developing computer models that simulate how lithium-ion batteries behave under different conditions, helping engineers design better and longer-lasting batteries. The project introduces two main models: an electrical model that predicts how energy flows through a battery and a thermal model that estimates how the battery heats up during use. The electrical model simplifies complex battery behavior by representing it with basic circuit components, while the thermal model uses advanced calculations to simulate how heat spreads within the battery. By combining these models, the research creates a tool that can predict how batteries perform over time and how temperature changes affect their efficiency and lifespan. One of the key achievements of this work is improving the speed and accuracy of these simulations. The thermal model was enhanced to calculate heat distribution more efficiently, cutting simulation times in half. The model was also validated against industry-standard tools like Ansys™, with results showing temperature predictions within a 2-3°C margin of error. This tool can simulate battery packs of any size, making it valuable for designing electric vehicle batteries and large-scale energy storage systems. By identifying potential issues like overheating or uneven energy distribution, the model helps engineers develop safer and more reliable battery technologies. Ultimately, this research contributes to the advancement of energy storage systems, supporting the transition to cleaner and more sustainable energy solutions for the future. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:42439 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/124317 | |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Battery Electric Vehicles | en |
| dc.subject | Cell Modeling | en |
| dc.subject | Equivalent Circuit Modeling | en |
| dc.subject | Busbar Modeling | en |
| dc.subject | Cell Thermal Modeling | en |
| dc.title | A tool for analyzing the evolution of non-uniformities in lithium-ion cylindrical battery cells at the module level under various operating conditions | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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